
# Installing the libraries with the specified version.
!pip install pandas==1.5.3 numpy==1.25.2 matplotlib==3.7.1 seaborn==0.13.1 scikit-learn==1.2.2 statsmodels==0.14.1 -q --user


# this will help in making the Python code more structured automatically (help adhere to good coding practices)
#%load_ext nb_black

import warnings
warnings.filterwarnings("ignore")
from statsmodels.tools.sm_exceptions import ConvergenceWarning

warnings.simplefilter("ignore", ConvergenceWarning)

# Libraries to help with reading and manipulating data
import pandas as pd
import numpy as np


# libaries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

# Library to split data
from sklearn.model_selection import train_test_split

# To build model for prediction
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant
from sklearn.linear_model import LogisticRegression

# To tune different models
from sklearn.model_selection import GridSearchCV

# To get diferent metric scores
from sklearn.metrics import (
    f1_score,
    accuracy_score,
    recall_score,
    precision_score,
    confusion_matrix,
    make_scorer,  roc_auc_score,
    ConfusionMatrixDisplay,
    precision_recall_curve,
    roc_curve,
)


df_main = pd.read_csv("INNHotelsGroup.csv")


# copying data to another variable to avoid any changes to original data
data = df_main.copy()


data.head()


#checking unique values for type meal plan
meal_plan = data['type_of_meal_plan'].unique()
#checking unique values for room_type_reserved
room_type_reserved = data['room_type_reserved'].unique()
#checking unique values for market_segment_type
market_segment_type = data['market_segment_type'].unique()
#checking unique values for booking_status
booking_status = data['booking_status'].unique()
print('type_of_meal_plan values are',meal_plan)
print('room_type_reserved unique values are',room_type_reserved)
print('market_segment_type unique values are',market_segment_type)
print('booking_status unique values are',booking_status)

type_of_meal_plan values are ['Meal Plan 1' 'Not Selected' 'Meal Plan 2' 'Meal Plan 3']
room_type_reserved unique values are ['Room_Type 1' 'Room_Type 4' 'Room_Type 2' 'Room_Type 6' 'Room_Type 5'
 'Room_Type 7' 'Room_Type 3']
market_segment_type unique values are ['Offline' 'Online' 'Corporate' 'Aviation' 'Complementary']
booking_status unique values are ['Not_Canceled' 'Canceled']


data['booking_status'].unique()

array(['Not_Canceled', 'Canceled'], dtype=object)


data['booking_status'].value_counts()

Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64


data = data.drop('Booking_ID', axis=1)  # Remove 'Booking_ID' column (axis=1 for columns)


data.shape

(36275, 18)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 18 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          36275 non-null  int64  
 1   no_of_children                        36275 non-null  int64  
 2   no_of_weekend_nights                  36275 non-null  int64  
 3   no_of_week_nights                     36275 non-null  int64  
 4   type_of_meal_plan                     36275 non-null  object 
 5   required_car_parking_space            36275 non-null  int64  
 6   room_type_reserved                    36275 non-null  object 
 7   lead_time                             36275 non-null  int64  
 8   arrival_year                          36275 non-null  int64  
 9   arrival_month                         36275 non-null  int64  
 10  arrival_date                          36275 non-null  int64  
 11  market_segment_type                   36275 non-null  object 
 12  repeated_guest                        36275 non-null  int64  
 13  no_of_previous_cancellations          36275 non-null  int64  
 14  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 15  avg_price_per_room                    36275 non-null  float64
 16  no_of_special_requests                36275 non-null  int64  
 17  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(4)
memory usage: 5.0+ MB


data.isnull().sum()

no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64


data.duplicated().sum()

10275


data.describe(include='all').T


data.nunique()

no_of_adults                               5
no_of_children                             6
no_of_weekend_nights                       8
no_of_week_nights                         18
type_of_meal_plan                          4
required_car_parking_space                 2
room_type_reserved                         7
lead_time                                352
arrival_year                               2
arrival_month                             12
arrival_date                              31
market_segment_type                        5
repeated_guest                             2
no_of_previous_cancellations               9
no_of_previous_bookings_not_canceled      59
avg_price_per_room                      3930
no_of_special_requests                     6
booking_status                             2
dtype: int64


data['no_of_children'].value_counts()

0     33577
1      1618
2      1058
3        19
9         2
10        1
Name: no_of_children, dtype: int64


data.columns

Index(['no_of_adults', 'no_of_children', 'no_of_weekend_nights',
       'no_of_week_nights', 'type_of_meal_plan', 'required_car_parking_space',
       'room_type_reserved', 'lead_time', 'arrival_year', 'arrival_month',
       'arrival_date', 'market_segment_type', 'repeated_guest',
       'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled',
       'avg_price_per_room', 'no_of_special_requests', 'booking_status'],
      dtype='object')


#Defining custom variable t plot combine histogram and box plot
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to create labeled barplots


def labeled_barplot(data, feature, perc=True, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


#Defining function to plot stacked barplot
def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


# function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


# Check for continuous data types
continuous_cols = data.select_dtypes(include=[np.number])  # Select numeric columns

# Loop through continuous columns
for col in continuous_cols.columns:
    plt.figure()  # Create a new figure for each plot
    histogram_boxplot(data, col)  # Pass the column name (feature)

    # Extract the column title from the DataFrame
    col_title = data.columns.get_loc(col)  # Get the column index and use it to retrieve the title
    plt.title(col_title)  # Set the plot title using the column title
    plt.show()

<Figure size 640x480 with 0 Axes>

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# Check for categorical data types
categorical_cols = data.select_dtypes(include=['object'])  # Select object columns (categorical data)


# Loop through categorical columns
for col in categorical_cols.columns:
   plt.figure()  # Create a new figure for each plot

   labeled_barplot(data, col)  # Plot bar chart of categorical variables

   plt.show();

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# creating a list of numerical columns
num_cols = data.select_dtypes(include=np.number).columns.tolist()


#plotting correlation heat map
plt.figure(figsize=(12, 7))
sns.heatmap(
    data[num_cols].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")

plt.show()


plt.figure()
distribution_plot_wrt_target(data,'avg_price_per_room' ,'market_segment_type')
plt.show();

<Figure size 640x480 with 0 Axes>


plt.figure()
distribution_plot_wrt_target(data,'no_of_previous_cancellations','repeated_guest')
plt.show();

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plt.figure()
distribution_plot_wrt_target(data,'no_of_special_requests','repeated_guest')
plt.show();

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plt.figure()
distribution_plot_wrt_target(data,'lead_time' ,'booking_status')
plt.show();

<Figure size 640x480 with 0 Axes>


plt.figure()
sns.countplot(data, x = 'market_segment_type', hue ='booking_status', )
plt.show();


plt.figure()
sns.countplot(data, x = 'arrival_month', hue ='booking_status', )
plt.show();


numerical_col = data.select_dtypes(include=np.number).columns.tolist()
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(data[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


# functions to treat outliers by flooring and capping


def treat_outliers(df, col):
    """
    Treats outliers in a variable

    df: dataframe
    col: dataframe column
    """
    Q1 = df[col].quantile(0.25)  # 25th quantile
    Q3 = df[col].quantile(0.75)  # 75th quantile
    IQR = Q3 - Q1
    Lower_Whisker = Q1 - 1.5 * IQR
    Upper_Whisker = Q3 + 1.5 * IQR

    # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker
    # all the values greater than Upper_Whisker will be assigned the value of Upper_Whisker
    df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker)

    return df


def treat_outliers_all(df, col_list):
    """
    Treat outliers in a list of variables

    df: dataframe
    col_list: list of dataframe columns
    """
    for c in col_list:
        df = treat_outliers(df, c)

    return df


numerical_col = data.select_dtypes(include=np.number).columns.tolist()
dt = treat_outliers_all(data, numerical_col)


# let's look at box plot to see if outliers have been treated or not
plt.figure(figsize=(20, 30))

for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(dt[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


data['booking_status'].value_counts()

Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64


dt['booking_status'].value_counts()

Not_Canceled    24390
Canceled        11885
Name: booking_status, dtype: int64


data1 = data.copy()


# Converting Boolean variables in booking status column to numeric variables for data set without treated outliers
data1['booking_status'] = data1['booking_status'].replace({'Canceled': 1, 'Not_Canceled': 0})
data1['booking_status'].value_counts()

0    24390
1    11885
Name: booking_status, dtype: int64


# Converting Boolean variables in booking status column to numeric variables for data set without treated outliers
dt['booking_status'] = dt['booking_status'].replace({'Canceled': 1, 'Not_Canceled': 0})
dt['booking_status'].value_counts()

0    24390
1    11885
Name: booking_status, dtype: int64


import pandas as pd

def create_dummies(data, specific_cols):
    """
    Creates dummy variables for specific columns in a pandas DataFrame.

    Args:
        data: The pandas DataFrame.
        specific_cols (list): A list of column names to convert to dummy variables.

    Returns:
        pandas.DataFrame: The DataFrame with new dummy variables.
    """

    # Create dummy variables for the specified columns
    dummies = pd.get_dummies(data[specific_cols], drop_first=True)

    # Drop the original columns from the DataFrame
    data = data.drop(columns=specific_cols)

    # Combine original data without the specified columns with dummy variables
    return pd.concat([data, dummies], axis=1)


specific_cols = ['type_of_meal_plan', 'room_type_reserved', 'market_segment_type']  # Columns to convert to dummies
df = create_dummies(data1.copy(), specific_cols)  # Saving data (outliers not treated) with dummy variables

# saving dt (outlier-treated) with dummy variables
dt1 = create_dummies(dt.copy(), specific_cols)

df

dt1


df.duplicated().sum()

10615


# Remove all duplicate rows
df = df.drop_duplicates()

df.duplicated().sum()

0


df.describe(include='all').T


dt1.duplicated().sum()

10615


# Remove all duplicate rows
dt1 = dt1.drop_duplicates()

dt1.duplicated().sum()

0


dt1.describe(include='all').T


import statsmodels.api as sm
# Assigning variable to data without outliers treated
A = df.drop(["booking_status"], axis=1)
B = df["booking_status"]

# Assigning variable data with outliers treated
X = dt1.drop(["booking_status"], axis=1)
Y = dt1["booking_status"]


A = sm.add_constant(A)
X = sm.add_constant(X)

# Splitting data without outliers treated into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1
)

# Splitting data with outliers treated into train and test sets
A_train, A_test, B_train, B_test = train_test_split(
    A, B, test_size=0.30, random_state=1
)


X.nunique()

no_of_adults                               1
no_of_children                             1
no_of_weekend_nights                       6
no_of_week_nights                          7
required_car_parking_space                 1
lead_time                                291
arrival_year                               1
arrival_month                             12
arrival_date                              31
repeated_guest                             1
no_of_previous_cancellations               1
no_of_previous_bookings_not_canceled       1
avg_price_per_room                      3513
no_of_special_requests                     4
type_of_meal_plan_Meal Plan 2              2
type_of_meal_plan_Meal Plan 3              2
type_of_meal_plan_Not Selected             2
room_type_reserved_Room_Type 2             2
room_type_reserved_Room_Type 3             2
room_type_reserved_Room_Type 4             2
room_type_reserved_Room_Type 5             2
room_type_reserved_Room_Type 6             2
room_type_reserved_Room_Type 7             2
market_segment_type_Complementary          2
market_segment_type_Corporate              2
market_segment_type_Offline                2
market_segment_type_Online                 2
dtype: int64


print("Shape of Training set with outliers not treated : ", X_train.shape)
print("Shape of test set with outliers not treated : ", X_test.shape)
print("Percentage of classes in training set:")
print(y_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(y_test.value_counts(normalize=True))

Shape of Training set with outliers not treated :  (17962, 27)
Shape of test set with outliers not treated :  (7698, 27)
Percentage of classes in training set:
0   0.71456
1   0.28544
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.70746
1   0.29254
Name: booking_status, dtype: float64


print("Shape of Training set with outliers  treated : ", A_train.shape)
print("Shape of test set with outliers treated : ", A_test.shape)
print("Percentage of classes in training set:")
print(B_train.value_counts(normalize=True))
print("Percentage of classes in test set:")
print(B_test.value_counts(normalize=True))

Shape of Training set with outliers  treated :  (17962, 27)
Shape of test set with outliers treated :  (7698, 27)
Percentage of classes in training set:
0   0.71456
1   0.28544
Name: booking_status, dtype: float64
Percentage of classes in test set:
0   0.70746
1   0.29254
Name: booking_status, dtype: float64


def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to create labeled barplots


def labeled_barplot(data, feature, perc=True, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()


### function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data[data[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data[data[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()


# Check for continuous data types
continuous_cols = df.select_dtypes(include=[np.number])  # Select numeric columns

# Loop through continuous columns
for col in continuous_cols.columns:
    plt.figure()  # Create a new figure for each plot
    histogram_boxplot(df, col)  # Pass the column name (feature)

    # Extract the column title from the DataFrame
    col_title = df.columns.get_loc(col)  # Get the column index and use it to retrieve the title
    plt.title(col_title)  # Set the plot title using the column title
    plt.show()

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<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>


# Check for categorical data types
categorical_cols = df.select_dtypes(include=['object'])  # Select object columns (categorical data)


# Loop through categorical columns
for col in categorical_cols.columns:
   plt.figure()  # Create a new figure for each plot

   labeled_barplot(df, col)  # Plot bar chart of categorical variables

   plt.show();


labeled_barplot(df,'booking_status', perc=True, n=None)


# creating a list of numerical columns
num_cols = df.select_dtypes(include=np.number).columns.tolist()


#plotting correlation heat map
plt.figure(figsize=(12, 7))
sns.heatmap(
    df[num_cols].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")

plt.show()


####Plotting Arrival month vs booking status.
plt.figure()
sns.countplot(df, x = 'arrival_month', hue ='booking_status', )
plt.show();


plt.figure()
sns.countplot(df, x= 'no_of_special_requests', hue ='booking_status', )
plt.show();


plt.figure()
sns.countplot(df, x = 'market_segment_type_Online', hue ='booking_status', )
plt.show();


plt.figure()
sns.boxplot(df, y = 'avg_price_per_room', x ='booking_status', )
plt.show();


plt.figure()
sns.boxplot(df, y = 'lead_time', x ='booking_status', )
plt.show();


from statsmodels.stats.outliers_influence import variance_inflation_factor


def checking_vif(predictors):
    vif = pd.DataFrame()
    vif["feature"] = predictors.columns

    # calculating VIF for each feature
    vif["VIF"] = [
        variance_inflation_factor(predictors.values, i)
        for i in range(len(predictors.columns))
    ]
    return vif


checking_vif(X_train)


checking_vif(A_train)


#Defining a variable to remove multicoolinearity
def treating_multicollinearity(predictors, target, high_vif_columns):
    """
    Checking the effect of dropping the columns showing high multicollinearity
    on model performance (adj. R-squared and RMSE)

    predictors: independent variables
    target: dependent variable
    high_vif_columns: columns having high VIF
    """
    # empty lists to store adj. R-squared and RMSE values
    adj_r2 = []
    rmse = []

    # build ols models by dropping one of the high VIF columns at a time
    # store the adjusted R-squared and RMSE in the lists defined previously
    for cols in high_vif_columns:
        # defining the new train set
        train = predictors.loc[:, ~predictors.columns.str.startswith(cols)]

        # create the model
        olsmodel = sm.OLS(target, train).fit()

        # adding adj. R-squared and RMSE to the lists
        adj_r2.append(olsmodel.rsquared_adj)
        rmse.append(np.sqrt(olsmodel.mse_resid))

    # creating a dataframe for the results
    temp = pd.DataFrame(
        {
            "col": high_vif_columns,
            "Adj. R-squared after_dropping col": adj_r2,
            "RMSE after dropping col": rmse,
        }
    ).sort_values(by="Adj. R-squared after_dropping col", ascending=False)
    temp.reset_index(drop=True, inplace=True)

    return temp


#checking impact on model when feature with multi colinearity is removed
col_list1 = ["no_of_children", "required_car_parking_space"]

res1 = treating_multicollinearity(A_train, B_train, col_list1)
res1


#Dropping feature with multi colinearity
col_to_drop1 = "no_of_children"
A_train = A_train.drop(col_to_drop1, axis=1)
A_test = A_test.drop(col_to_drop1, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop1)
vif

VIF after dropping  no_of_children


#checking impact on model when feature with multi colinearity is removed
col_list2 = ["repeated_guest", "required_car_parking_space"]

res2 = treating_multicollinearity(A_train, B_train, col_list2)
res2


#Dropping feature with multi colinearity
col_to_drop2 = "required_car_parking_space"
A_train = A_train.drop(col_to_drop2, axis=1)
A_test = A_test.drop(col_to_drop2, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop2)
vif

VIF after dropping  required_car_parking_space


#Dropping feature with multi colinearity
col_to_drop3 = "no_of_previous_bookings_not_canceled"
A_train = A_train.drop(col_to_drop3, axis=1)
A_test = A_test.drop(col_to_drop3, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop3)
vif

VIF after dropping  no_of_previous_bookings_not_canceled


#Dropping feature with multi colinearity
col_to_drop4 = "repeated_guest"
A_train = A_train.drop(col_to_drop4, axis=1)
A_test = A_test.drop(col_to_drop4, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop4)
vif

VIF after dropping  repeated_guest


#Dropping feature with multi colinearity
col_to_drop5 = "no_of_previous_cancellations"
A_train = A_train.drop(col_to_drop5, axis=1)
A_test = A_test.drop(col_to_drop5, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop5)
vif

VIF after dropping  no_of_previous_cancellations


#checking impact on model when feature with multi colinearity is removed
col_list3 = ["market_segment_type_Offline", "market_segment_type_Online"]

res3 = treating_multicollinearity(A_train, B_train, col_list3)
res3


#Dropping feature with multi colinearity
col_to_drop6 = "market_segment_type_Offline"
A_train = A_train.drop(col_to_drop6, axis=1)
A_test = A_test.drop(col_to_drop6, axis=1)

# Check VIF now
vif = checking_vif(A_train)
print("VIF after dropping ", col_to_drop6)
vif

VIF after dropping  market_segment_type_Offline


A_test.shape

(7698, 21)


# fitting logistic regression model
logit = sm.Logit(B_train, A_train.astype(float))
lg = logit.fit(disp=False)

print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                17962
Model:                          Logit   Df Residuals:                    17941
Method:                           MLE   Df Model:                           20
Date:                Sat, 16 Mar 2024   Pseudo R-squ.:                  0.3072
Time:                        23:45:18   Log-Likelihood:                -7441.7
converged:                      False   LL-Null:                       -10742.
Covariance Type:            nonrobust   LLR p-value:                     0.000
=====================================================================================================
                                        coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------------------
no_of_adults                      -2.324e-06        nan        nan        nan         nan         nan
no_of_weekend_nights                  0.0982      0.023      4.188      0.000       0.052       0.144
no_of_week_nights                     0.0801      0.016      5.134      0.000       0.050       0.111
lead_time                             0.0164      0.000     46.406      0.000       0.016       0.017
arrival_year                         -0.0025        nan        nan        nan         nan         nan
arrival_month                        -0.0506      0.007     -7.022      0.000      -0.065      -0.036
arrival_date                         -0.0010      0.002     -0.439      0.660      -0.006       0.004
avg_price_per_room                    0.0191      0.001     21.637      0.000       0.017       0.021
no_of_special_requests               -1.5065      0.034    -44.698      0.000      -1.573      -1.440
type_of_meal_plan_Meal Plan 2        -0.0957      0.105     -0.911      0.362      -0.302       0.110
type_of_meal_plan_Meal Plan 3        16.5861   1941.805      0.009      0.993   -3789.282    3822.454
type_of_meal_plan_Not Selected        0.3716      0.057      6.467      0.000       0.259       0.484
room_type_reserved_Room_Type 2       -0.3200      0.138     -2.324      0.020      -0.590      -0.050
room_type_reserved_Room_Type 3       -0.7997      1.802     -0.444      0.657      -4.332       2.733
room_type_reserved_Room_Type 4       -0.0808      0.057     -1.428      0.153      -0.192       0.030
room_type_reserved_Room_Type 5       -0.5014      0.233     -2.156      0.031      -0.957      -0.046
room_type_reserved_Room_Type 6       -0.3529      0.117     -3.011      0.003      -0.583      -0.123
room_type_reserved_Room_Type 7       -0.3479      0.292     -1.190      0.234      -0.921       0.225
market_segment_type_Complementary   -28.8719   7813.339     -0.004      0.997   -1.53e+04    1.53e+04
market_segment_type_Corporate         1.2531      0.142      8.802      0.000       0.974       1.532
market_segment_type_Online            2.1726      0.078     27.746      0.000       2.019       2.326
=====================================================================================================


import statsmodels.api as sm

# initial list of columns
predictors = A_train.copy()
cols = predictors.columns.tolist()

# setting an initial max p-value
max_p_value = 1

while len(cols) > 0:
    # defining the train set
    A_train_aux = predictors[cols]

    # fitting the model
    model = sm.Logit(B_train, A_train_aux.astype(float))
    model = model.fit(disp=False)

    # getting the p-values and the maximum p-value
    p_values = model.pvalues
    max_p_value = p_values.max()

    # name of the variable with maximum p-value
    feature_with_p_max = p_values.idxmax()

    if max_p_value > 0.05:
        cols.remove(feature_with_p_max)
    else:
        break


selected_features = cols
print(selected_features)

['no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_year', 'arrival_month', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'market_segment_type_Corporate', 'market_segment_type_Online']


A_train2 = A_train[selected_features]


A_test2 = A_test[selected_features]


# fitting logistic regression model
logit2 = sm.Logit(B_train, A_train2.astype(float))
lg2 = logit2.fit(disp=False)

print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                17962
Model:                          Logit   Df Residuals:                    17949
Method:                           MLE   Df Model:                           12
Date:                Sat, 16 Mar 2024   Pseudo R-squ.:                  0.3067
Time:                        23:47:17   Log-Likelihood:                -7446.7
converged:                       True   LL-Null:                       -10742.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
no_of_weekend_nights               0.0967      0.023      4.128      0.000       0.051       0.143
no_of_week_nights                  0.0773      0.015      4.988      0.000       0.047       0.108
lead_time                          0.0164      0.000     46.611      0.000       0.016       0.017
arrival_year                      -0.0025    6.1e-05    -40.768      0.000      -0.003      -0.002
arrival_month                     -0.0498      0.007     -6.942      0.000      -0.064      -0.036
avg_price_per_room                 0.0185      0.001     23.337      0.000       0.017       0.020
no_of_special_requests            -1.5070      0.034    -44.718      0.000      -1.573      -1.441
type_of_meal_plan_Not Selected     0.3919      0.056      6.995      0.000       0.282       0.502
room_type_reserved_Room_Type 2    -0.3006      0.137     -2.198      0.028      -0.569      -0.033
room_type_reserved_Room_Type 5    -0.4658      0.231     -2.012      0.044      -0.920      -0.012
room_type_reserved_Room_Type 6    -0.2917      0.111     -2.634      0.008      -0.509      -0.075
market_segment_type_Corporate      1.2690      0.142      8.961      0.000       0.991       1.547
market_segment_type_Online         2.1820      0.077     28.437      0.000       2.032       2.332
==================================================================================================


# converting coefficients to odds
odds = np.exp(lg2.params)

# finding the percentage change
perc_change_odds = (np.exp(lg2.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index= A_train2.columns).T


# converting coefficients to odds
odds = np.exp(lg2.params)

# finding the percentage change
perc_change_odds = (np.exp(lg2.params) - 1) * 100

# removing limit from number of columns to display
pd.set_option("display.max_columns", None)

# adding the odds to a dataframe
pd.DataFrame({"Odds": odds, "Change_odd%": perc_change_odds}, index= A_test2.columns).T


# defining a function to compute different metrics to check performance of a classification model built using statsmodels
def model_performance_classification_statsmodels(
    model, predictors, target, threshold=0.5
):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """

    # checking which probabilities are greater than threshold
    pred_temp = model.predict(predictors) > threshold
    # rounding off the above values to get classes
    pred = np.round(pred_temp)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


# defining a function to plot the confusion_matrix of a classification model


def confusion_matrix_statsmodels(model, predictors, target, threshold=0.5):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    threshold: threshold for classifying the observation as class 1
    """
    y_pred = model.predict(predictors) > threshold
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


log_reg_model_train_perf = confusion_matrix_statsmodels(lg2, A_train2, B_train)
log_reg_model_train_perf


print("Training performance:")

Logistic_regression_model_train_2 = model_performance_classification_statsmodels(lg2, A_train2, B_train)

Logistic_regression_model_train_2

Training performance:


log_reg_model_test_perf = confusion_matrix_statsmodels(lg2, A_test2, B_test)


print("Test performance:")
Logistic_regression_model_test_2 = model_performance_classification_statsmodels(lg2, A_test2, B_test)
Logistic_regression_model_test_2

Test performance:


y_scores = lg2.predict(A_train2)
prec, rec, tre = precision_recall_curve(B_train, y_scores,)


def plot_prec_recall_vs_tresh(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="precision")
    plt.plot(thresholds, recalls[:-1], "g--", label="recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])


plt.figure(figsize=(10, 7))
plot_prec_recall_vs_tresh(prec, rec, tre)
plt.show()


# setting the threshold
optimal_threshold_curve = 0.64


# creating confusion matrix
confusion_matrix_statsmodels(lg2, A_train2, B_train, threshold=optimal_threshold_curve)


Logistic_regression_model_train_3 = model_performance_classification_statsmodels(
    lg2, A_train2, B_train, threshold=optimal_threshold_curve
)
print("Training performance:")
Logistic_regression_model_train_3

Training performance:


# creating confusion matrix
confusion_matrix_statsmodels(lg2, A_test2, y_test, threshold=optimal_threshold_curve)


Logistic_regression_model_test_3 = model_performance_classification_statsmodels(
    lg2, A_test2, B_test, threshold=optimal_threshold_curve
)
print("Test performance:")
Logistic_regression_model_test_3

Test performance:


# Optimal threshold as per AUC-ROC curve
# The optimal cut off would be where tpr is high and fpr is low
fpr, tpr, thresholds = roc_curve(B_train, lg2.predict(A_train2))

optimal_idx = np.argmax(tpr - fpr)
optimal_threshold_auc_roc = thresholds[optimal_idx]
print(optimal_threshold_auc_roc)

0.29901073654427324


# creating confusion matrix
confusion_matrix_statsmodels(
    lg2, A_train2, B_train, threshold=optimal_threshold_auc_roc
)


# checking model performance for this model
Logistic_regression_model_train_4 = model_performance_classification_statsmodels(
    lg2, A_train2, B_train, threshold=optimal_threshold_auc_roc
)
print("Training performance:")
Logistic_regression_model_train_4

Training performance:


logit_roc_auc_train = roc_auc_score(B_test, lg2.predict(A_test2))
fpr, tpr, thresholds = roc_curve(B_test, lg2.predict(A_test2))
plt.figure(figsize=(7, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()


# creating confusion matrix
confusion_matrix_statsmodels(lg2, A_test2, B_test, threshold=optimal_threshold_auc_roc)


# checking model performance for this model
Logistic_regression_model_test_4 = model_performance_classification_statsmodels(
    lg2, A_test2, B_test, threshold=optimal_threshold_auc_roc
)
print("Test performance:")
Logistic_regression_model_test_4

Test performance:


# training performance comparison

models_train_comp_df = pd.concat(
    [
        Logistic_regression_model_train_2 .T,
        Logistic_regression_model_test_3.T,
        Logistic_regression_model_train_4.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.64 Threshold",
    "Logistic Regression-0.29 Threshold",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.concat(
    [
        Logistic_regression_model_test_2.T,
        Logistic_regression_model_test_3.T,
        Logistic_regression_model_test_4.T,
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Logistic Regression-default Threshold (0.5)",
    "Logistic Regression-0.64 Threshold",
    "Logistic Regression-0.29 Threshold",
]

print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:


# Set the threshold
threshold = 0.29

# Convert coefficients to odds
odds = np.exp(lg2.params)

# Filter coefficients that are above the threshold
significant_odds = odds[odds > threshold]

# Finding the percentage change for significant coefficients
perc_change_odds = (significant_odds - 1) * 100

# Create a DataFrame with significant coefficients
significant_coefficients_df = pd.DataFrame({"Odds": significant_odds, "Change_odd%": perc_change_odds}, index=A_train2.columns)

# Display the DataFrame
significant_coefficients_df.T


# Set the threshold
threshold = 0.29

# Convert coefficients to odds
odds = np.exp(lg2.params)

# Filter coefficients that are above the threshold
significant_odds = odds[odds > threshold]

# Finding the percentage change for significant coefficients
perc_change_odds = (significant_odds - 1) * 100

# Create a DataFrame with significant coefficients
significant_coefficients_df = pd.DataFrame({"Odds": significant_odds, "Change_odd%": perc_change_odds}, index=A_test2.columns)

# Display the DataFrame
significant_coefficients_df.T


#Building default decision tree
model0 = DecisionTreeClassifier(random_state=1)
model0.fit(X_train, y_train)

DecisionTreeClassifier(random_state=1)

DecisionTreeClassifier(random_state=1)


# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {"Accuracy": acc, "Recall": recall, "Precision": precision, "F1": f1,},
        index=[0],
    )

    return df_perf


def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")


confusion_matrix_sklearn(model0, X_train, y_train)


decision_tree_perf_train_without = model_performance_classification_sklearn(
    model0, X_train, y_train
)
decision_tree_perf_train_without


confusion_matrix_sklearn(model0, X_test, y_test)


decision_tree_perf_test_without = model_performance_classification_sklearn(
    model0, X_test, y_test
)
decision_tree_perf_test_without


model1 = DecisionTreeClassifier(random_state=1, class_weight="balanced")
model1.fit(X_train, y_train)

DecisionTreeClassifier(class_weight='balanced', random_state=1)

DecisionTreeClassifier(class_weight='balanced', random_state=1)


confusion_matrix_sklearn(model1, X_train, y_train)


decision_tree_perf_train = model_performance_classification_sklearn(
    model1, X_train, y_train
)
decision_tree_perf_train


confusion_matrix_sklearn(model1, X_test, y_test)


decision_tree_perf_test = model_performance_classification_sklearn(
    model1, X_test, y_test
)
decision_tree_perf_test


# Choose the type of classifier.
estimator = DecisionTreeClassifier(random_state=1)

# Grid of parameters to choose from
parameters = {
    "class_weight": [None, "balanced"],
    "max_depth": np.arange(2, 7, 2),
    "max_leaf_nodes": [50, 75, 150, 250],
    "min_samples_split": [10, 30, 50, 70],
}

# Type of scoring used to compare parameter combinations
acc_scorer = make_scorer(recall_score)

# Run the grid search
grid_obj = GridSearchCV(estimator, parameters, scoring=acc_scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
estimator = grid_obj.best_estimator_
model2 = estimator

# Fit the best algorithm to the data.
model2.fit(X_train, y_train)

DecisionTreeClassifier(class_weight='balanced', max_depth=2, max_leaf_nodes=50,
                       min_samples_split=10, random_state=1)

DecisionTreeClassifier(class_weight='balanced', max_depth=2, max_leaf_nodes=50,
                       min_samples_split=10, random_state=1)


#Visualizing confusion matrix for training data
confusion_matrix_sklearn(model2, X_train, y_train)


decision_tree_tune_perf_train = model_performance_classification_sklearn(
    model2, X_train, y_train
)
decision_tree_tune_perf_train


#Visualizing confusion matrix for test data
confusion_matrix_sklearn(model2, X_test, y_test)


decision_tree_tune_perf_test = model_performance_classification_sklearn(
    model2, X_test, y_test
)
decision_tree_tune_perf_test


# Writing fubction to visualize decision tree built
feature_names = list(X_train.columns)
importances = model1.feature_importances_
indices = np.argsort(importances)


plt.figure(figsize=(20, 10))
out = tree.plot_tree(
    model1,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
# below code will add arrows to the decision tree split if they are missing
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


importances = model1.feature_importances_
importances

array([0.00000000e+00, 0.00000000e+00, 3.94350680e-02, 5.74059421e-02,
       0.00000000e+00, 3.15897423e-01, 0.00000000e+00, 7.92628968e-02,
       9.53611457e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
       1.38250333e-01, 1.22261786e-01, 3.64648127e-03, 0.00000000e+00,
       1.18380955e-02, 2.74383483e-03, 0.00000000e+00, 1.24454264e-02,
       6.71302943e-04, 2.75637334e-03, 3.02490718e-04, 1.29826380e-04,
       1.44599624e-03, 8.18416572e-04, 1.15327162e-01])


# Text report showing the rules of a decision tree -
print(tree.export_text(model1, feature_names=feature_names, show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 92.50
|   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.44
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [247.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.44
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 37.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  37.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 41.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  41.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.20
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [15.39, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 27.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.10
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.10
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- lead_time >  27.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 97.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.90, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  97.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 24.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  24.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.25
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [27.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.25
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 69.75
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  69.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [135.75, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [165.14, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |--- market_segment_type_Complementary <= 0.50
|   |   |   |   |   |   |--- lead_time <= 9.00
|   |   |   |   |   |   |   |--- weights: [0.00, 8.76] class: 1
|   |   |   |   |   |   |--- lead_time >  9.00
|   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |--- market_segment_type_Complementary >  0.50
|   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |--- lead_time >  92.50
|   |   |   |   |--- avg_price_per_room <= 113.53
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |--- weights: [16.09, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |--- lead_time <= 116.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.12
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.12
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  116.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  75.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 146.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  146.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 101.02
|   |   |   |   |   |   |   |--- weights: [27.29, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  101.02
|   |   |   |   |   |   |   |--- avg_price_per_room <= 104.08
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  104.08
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  113.53
|   |   |   |   |   |--- avg_price_per_room <= 138.25
|   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |--- lead_time <= 119.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  119.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 14.01] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 130.54
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 122.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  122.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  130.54
|   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  138.25
|   |   |   |   |   |   |--- avg_price_per_room <= 166.72
|   |   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  166.72
|   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 3.50
|   |   |   |   |--- avg_price_per_room <= 135.83
|   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 117.60
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [35.69, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  117.60
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.90
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.90
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |--- weights: [55.98, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.50
|   |   |   |   |   |   |   |   |   |--- weights: [27.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 91.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  91.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [8.40, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.33
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.01] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  135.83
|   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 161.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 136.09
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  136.09
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- arrival_date >  29.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  161.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 162.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  162.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.10, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- weights: [31.49, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.76] class: 1
|   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.01] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.26] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |--- lead_time >  3.50
|   |   |   |   |--- avg_price_per_room <= 105.28
|   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [52.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [77.67, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |--- avg_price_per_room <= 56.55
|   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 51.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  51.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 27.98
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  27.98
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |--- weights: [20.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 101.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  101.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  56.55
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 72.78
|   |   |   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.10
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  72.78
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.09
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 57.74
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  57.74
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.09
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.01
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.01
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 62.78
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  62.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |--- avg_price_per_room >  105.28
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 108.28
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  108.28
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 106.49
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  106.49
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 28.03] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 161.83
|   |   |   |   |   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  17.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  161.83
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.26] class: 1
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 167.42
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 167.02
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 32
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  167.02
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.90, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 149.02
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  149.02
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  167.42
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  7.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [32.19, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 169.05
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 46.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  46.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  169.05
|   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- lead_time <= 6.50
|   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |--- avg_price_per_room <= 80.90
|   |   |   |   |   |   |--- weights: [206.42, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  80.90
|   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 153.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 140.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  100.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  140.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [105.66, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  153.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 13.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [24.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_date >  13.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 87.01
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  87.01
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [134.35, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 133.17
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  133.17
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 7.01] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |--- lead_time >  6.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |--- weights: [270.79, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 132.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  132.00
|   |   |   |   |   |   |   |   |   |--- weights: [11.90, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 97.50
|   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  97.50
|   |   |   |   |   |   |   |   |--- lead_time <= 12.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  12.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |--- weights: [12.60, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.88
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 94.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  94.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.88
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  11.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 110.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  110.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 142.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  142.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- weights: [13.29, 0.00] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 118.74
|   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [62.28, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [86.07, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.49
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.29, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [42.68, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.90, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.49
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.26
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.26
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 19
|   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 106.66
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  106.66
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.79
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.79
|   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  118.74
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.58
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.58
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 2.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  2.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 12
|   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 17.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- lead_time >  17.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 126.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  126.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 157.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  157.95
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |   |   |--- weights: [50.38, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 179.11
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.51] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  179.11
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1345.58, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 4.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  4.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [25.89, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 99.57
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  99.57
|   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [16.09, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 82.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  82.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 122.70
|   |   |   |   |   |   |   |   |   |--- weights: [17.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  122.70
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  21.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [42.68, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |--- weights: [17.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.52
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 124.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  124.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.52
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 97.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  97.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.01] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 131.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.60
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  131.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.95
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.95
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- avg_price_per_room <= 140.49
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 134.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 84.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.01] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  84.80
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  134.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 100.50
|   |   |   |   |   |   |   |   |   |--- weights: [7.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  100.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  7.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |--- avg_price_per_room >  140.49
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.00
|   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.00
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 6 >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |--- weights: [51.08, 0.00] class: 0
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 99.89
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 272.00
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 81.76
|   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |--- weights: [21.69, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |--- lead_time <= 194.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.12
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 61.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  61.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.12
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- lead_time >  194.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 203.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  203.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |--- weights: [8.40, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  81.76
|   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 155.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  155.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.01] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 243.00
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  243.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [10.50, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.01] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- lead_time <= 151.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |--- lead_time >  151.50
|   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |--- weights: [42.68, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 185.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  185.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.30, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 1.75] class: 1
|   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |--- lead_time >  272.00
|   |   |   |   |--- avg_price_per_room <= 59.25
|   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  59.25
|   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_date <= 24.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 6.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_date >  6.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 68.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  68.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 90.33
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  90.33
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_date >  24.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.75
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 282.75
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  282.75
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.75
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- avg_price_per_room <= 35.22
|   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |--- arrival_date <= 22.00
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  22.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  35.22
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 50.87
|   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  50.87
|   |   |   |   |   |   |   |--- weights: [0.00, 557.04] class: 1
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 75.42
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  75.42
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.87
|   |   |   |   |   |   |   |   |   |--- lead_time <= 215.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  215.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 12.26] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.87
|   |   |   |   |   |   |   |   |   |--- lead_time <= 268.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  268.75
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 42.04] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 29.78] class: 1
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |--- lead_time <= 176.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |--- weights: [20.99, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  176.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.90, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [2.10, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 145.39] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |   |--- avg_price_per_room <= 72.00
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  72.00
|   |   |   |   |   |   |   |   |--- weights: [4.90, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 81.81
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 153.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  153.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [37.09, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  81.81
|   |   |   |   |   |   |   |--- avg_price_per_room <= 94.21
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 222.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  222.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 217.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  217.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  94.21
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 15.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  15.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 99.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  99.38
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 224.00
|   |   |   |   |   |   |   |   |   |--- weights: [4.20, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  224.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.19
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.19
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.50, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.76] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |--- avg_price_per_room >  99.89
|   |   |--- no_of_special_requests <= 2.25
|   |   |   |--- arrival_month <= 11.50
|   |   |   |   |--- avg_price_per_room <= 100.06
|   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.75] class: 1
|   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |--- lead_time <= 188.00
|   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.70, 1.75] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.50] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  188.00
|   |   |   |   |   |   |   |--- weights: [0.00, 21.02] class: 1
|   |   |   |   |--- avg_price_per_room >  100.06
|   |   |   |   |   |--- weights: [0.00, 1632.59] class: 1
|   |   |   |--- arrival_month >  11.50
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |--- weights: [25.19, 0.00] class: 0
|   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- arrival_date <= 24.50
|   |   |   |   |   |   |--- weights: [2.80, 0.00] class: 0
|   |   |   |   |   |--- arrival_date >  24.50
|   |   |   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 12.26] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 265.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 235.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.70, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  235.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  265.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.76] class: 1
|   |   |--- no_of_special_requests >  2.25
|   |   |   |--- weights: [21.69, 0.00] class: 0


# importance of features in the tree building

importances = model1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(8, 8))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# Define a Decision Tree Classifier with random state and class weight
clf = DecisionTreeClassifier(random_state=1, class_weight="balanced")
path = clf.cost_complexity_pruning_path(X_train, y_train)
ccp_alphas, impurities = abs(path.ccp_alphas), path.impurities


# Print the entire path information (for exploration purposes)
pd.DataFrame(path)


# List to store trained decision tree models
clfs = []
# Iterate over different alpha values obtained from pruning path
for ccp_alpha in ccp_alphas:
   # Create a new Decision Tree Classifier with current alpha and other parameters
    clf = DecisionTreeClassifier(
        random_state=1, ccp_alpha=ccp_alpha, class_weight="balanced"
    )
      # Train the model on the training data
    clf.fit(X_train, y_train)
    # Append the trained model to the list
    clfs.append(clf)
    # Print information about the last model in the list
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.05500091294910969


# Remove the last model from the list
clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

# Extract number of nodes and depth for each model
node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]

# Create a figure with two subplots
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
# Plot number of nodes vs alpha
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
# Plot number of nodes vs alpha
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
# Adjust layout to prevent overlapping elements
fig.tight_layout()


# Lists to store recall scores for training and testing data
recall_train = []
recall_test = []
# Calculate recall score on training data for each model
for clf in clfs:
    pred_train = clf.predict(X_train)
    values_train = recall_score(y_train, pred_train)
    recall_train.append(values_train)


# Calculate recall score on testing data for each model
for clf in clfs:
    pred_test = clf.predict(X_test)
    values_test = recall_score(y_test, pred_test)
    recall_test.append(values_test)


# Lists to store accuracy scores for training and testing data
train_scores = [clf.score(X_train, y_train) for clf in clfs]
test_scores = [clf.score(X_test, y_test) for clf in clfs]


# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
# Get the best model based on the index
best_model = clfs[index_best_model]
# Print details of the best model
print(best_model)

DecisionTreeClassifier(ccp_alpha=0.00023579767770967944,
                       class_weight='balanced', random_state=1)


# Confusion matrix for the best model on training data
confusion_matrix_sklearn(best_model, X_train, y_train)


print("Test set performance:")
decision_tree_post_perf_train = model_performance_classification_sklearn(
    best_model, X_train, y_train
)
decision_tree_post_perf_train

Test set performance:


confusion_matrix_sklearn(best_model, X_test, y_test)


print("Test set performance:")
decision_tree_post_test = model_performance_classification_sklearn(
    best_model, X_test, y_test
)
decision_tree_post_test

Test set performance:


# Plotting image report showing the rules of a decision tree -
plt.figure(figsize=(20, 10))

out = tree.plot_tree(
    best_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of a decision tree -

print(tree.export_text(best_model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 92.50
|   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.44
|   |   |   |   |   |   |   |   |   |--- weights: [285.49, 7.01] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.44
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 5.26] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- weights: [376.45, 105.10] class: 0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 27.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.10
|   |   |   |   |   |   |   |   |   |--- weights: [139.95, 21.02] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.10
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [39.88, 1.75] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.69, 1.75] class: 0
|   |   |   |   |   |   |   |--- lead_time >  27.50
|   |   |   |   |   |   |   |   |--- weights: [30.79, 31.53] class: 1
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- weights: [585.67, 45.54] class: 0
|   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |--- weights: [1.40, 10.51] class: 1
|   |   |   |--- lead_time >  92.50
|   |   |   |   |--- avg_price_per_room <= 113.53
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |--- weights: [16.09, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |--- lead_time <= 116.50
|   |   |   |   |   |   |   |   |--- weights: [69.97, 71.82] class: 1
|   |   |   |   |   |   |   |--- lead_time >  116.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [54.58, 22.77] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 14.01] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |--- weights: [30.09, 1.75] class: 0
|   |   |   |   |--- avg_price_per_room >  113.53
|   |   |   |   |   |--- weights: [13.29, 29.78] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 3.50
|   |   |   |   |--- avg_price_per_room <= 135.83
|   |   |   |   |   |--- weights: [298.08, 52.55] class: 0
|   |   |   |   |--- avg_price_per_room >  135.83
|   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [31.49, 17.52] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- weights: [31.49, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- weights: [9.10, 17.52] class: 1
|   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |--- weights: [5.60, 21.02] class: 1
|   |   |   |--- lead_time >  3.50
|   |   |   |   |--- avg_price_per_room <= 105.28
|   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [52.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [42.68, 54.30] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [24.49, 89.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.79, 5.26] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [60.88, 68.32] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [77.67, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |--- avg_price_per_room <= 56.55
|   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.90, 3.50] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.90, 17.52] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |--- weights: [22.39, 1.75] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  56.55
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 72.78
|   |   |   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [36.39, 59.56] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  72.78
|   |   |   |   |   |   |   |   |   |--- weights: [216.92, 632.37] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- weights: [83.97, 432.67] class: 1
|   |   |   |   |--- avg_price_per_room >  105.28
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [32.89, 136.63] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 161.83
|   |   |   |   |   |   |   |   |   |--- weights: [20.99, 8.76] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  161.83
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 12.26] class: 1
|   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |--- weights: [317.68, 2226.42] class: 1
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- weights: [9.80, 12.26] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [32.89, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |--- weights: [30.09, 87.59] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- lead_time <= 6.50
|   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |--- weights: [739.61, 68.32] class: 0
|   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |--- weights: [3.50, 7.01] class: 1
|   |   |   |--- lead_time >  6.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- weights: [282.69, 1.75] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- weights: [4.90, 7.01] class: 1
|   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |--- weights: [54.58, 22.77] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 118.74
|   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [62.28, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [473.72, 176.92] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [86.07, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.49
|   |   |   |   |   |   |   |   |   |--- weights: [62.28, 3.50] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.49
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.26
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.26
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |--- weights: [5.60, 38.54] class: 1
|   |   |   |   |   |--- avg_price_per_room >  118.74
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.58
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [97.26, 24.52] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.58
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [125.25, 80.58] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 8.76] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [57.38, 17.52] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [67.87, 71.82] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.80, 36.79] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 17.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.17, 38.54] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  17.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 126.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [128.75, 162.91] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  126.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [68.57, 147.14] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |   |   |--- weights: [50.38, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 10.51] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1345.58, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |--- weights: [34.29, 3.50] class: 0
|   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |--- weights: [16.09, 1.75] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.87, 63.06] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [46.18, 12.26] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [42.68, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |--- weights: [17.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.10, 15.77] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [143.44, 36.79] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [9.10, 10.51] class: 1
|   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |--- weights: [23.09, 22.77] class: 0
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- weights: [37.09, 45.54] class: 1
|   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |--- weights: [51.08, 0.00] class: 0
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 99.89
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 272.00
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 81.76
|   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |--- weights: [21.69, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |--- weights: [57.38, 31.53] class: 0
|   |   |   |   |   |--- avg_price_per_room >  81.76
|   |   |   |   |   |   |--- weights: [23.09, 31.53] class: 1
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- lead_time <= 151.50
|   |   |   |   |   |   |--- weights: [0.70, 3.50] class: 1
|   |   |   |   |   |--- lead_time >  151.50
|   |   |   |   |   |   |--- weights: [53.88, 5.26] class: 0
|   |   |   |--- lead_time >  272.00
|   |   |   |   |--- weights: [32.19, 45.54] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [9.80, 660.39] class: 1
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |   |--- weights: [32.89, 10.51] class: 0
|   |   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |   |--- weights: [7.00, 157.65] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 81.81
|   |   |   |   |   |   |   |--- weights: [59.48, 14.01] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  81.81
|   |   |   |   |   |   |   |--- weights: [107.76, 99.85] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [17.49, 36.79] class: 1
|   |--- avg_price_per_room >  99.89
|   |   |--- no_of_special_requests <= 2.25
|   |   |   |--- arrival_month <= 11.50
|   |   |   |   |--- weights: [4.20, 1660.62] class: 1
|   |   |   |--- arrival_month >  11.50
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- weights: [25.89, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- weights: [6.30, 29.78] class: 1
|   |   |--- no_of_special_requests >  2.25
|   |   |   |--- weights: [21.69, 0.00] class: 0


#identifying the most important features
importances = best_model.feature_importances_
indices = np.argsort(importances)


#Visualizing the most important features
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()


# training performance comparison

models_train_comp_df = pd.concat(
    [
        decision_tree_perf_train_without.T,
        decision_tree_perf_train.T,
        decision_tree_tune_perf_train.T,
        decision_tree_post_perf_train.T,
    ],
    axis=1,
)
models_train_comp_df.columns = [
    "Decision Tree without class_weight",
    "Decision Tree with class_weight",
    "Decision Tree (Pre-Pruning)",
    "decision_tree_post_train",
]
print("Training performance comparison:")
models_train_comp_df

Training performance comparison:


# testing performance comparison

models_test_comp_df = pd.concat(
    [
        decision_tree_perf_test_without.T,
        decision_tree_perf_test.T,
        decision_tree_tune_perf_test.T,
        decision_tree_post_test.T
    ],
    axis=1,
)
models_test_comp_df.columns = [
    "Decision Tree without class_weight",
    "Decision Tree with class_weight",
    "Decision Tree (Pre-Pruning)",
    "decision_tree_post_test"
]
print("Test set performance comparison:")
models_test_comp_df

Test set performance comparison:


#Assigning variable name to the best decision tree model
Final_decision_Tree_model = best_model


#Assigning a variable name to the best logistic regression model
Final_Regression_model = Logistic_regression_model_train_4


# training performance comparison


Final_models_train_comp = pd.concat(
    [
        decision_tree_post_perf_train.T,Logistic_regression_model_train_4.T
    ],
    axis=1,
)
Final_models_train_comp.columns = [

    "Decision Tree (Post-Pruning)", "Logistic_regression_model_train_4"
]
print("Training performance comparison:")
Final_models_train_comp

Training performance comparison:


# testing performance comparison


Final_models_test_comp = pd.concat(
    [
        decision_tree_post_test.T,Logistic_regression_model_test_4.T
    ],
    axis=1,
)
Final_models_test_comp.columns = [

    "decision_tree_post_test", "Logistic_regression_model_test_2"
]
print("Test performance comparison:")
Final_models_test_comp

Test performance comparison:


# Assighning best model selected to to final model variable.
final_model = Final_decision_Tree_model


# Plotting image report showing the rules of final model decision tree -
plt.figure(figsize=(20, 10))

out = tree.plot_tree(
    final_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=False,
    class_names=None,
)
for o in out:
    arrow = o.arrow_patch
    if arrow is not None:
        arrow.set_edgecolor("black")
        arrow.set_linewidth(1)
plt.show()


# Text report showing the rules of final model decision tree -

print(tree.export_text(final_model, feature_names=feature_names, show_weights=True))

|--- lead_time <= 150.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 92.50
|   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 178.44
|   |   |   |   |   |   |   |   |   |--- weights: [285.49, 7.01] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  178.44
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 5.26] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- weights: [376.45, 105.10] class: 0
|   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 27.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.10
|   |   |   |   |   |   |   |   |   |--- weights: [139.95, 21.02] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.10
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [39.88, 1.75] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 14.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  14.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.69, 1.75] class: 0
|   |   |   |   |   |   |   |--- lead_time >  27.50
|   |   |   |   |   |   |   |   |--- weights: [30.79, 31.53] class: 1
|   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |--- weights: [585.67, 45.54] class: 0
|   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |--- weights: [1.40, 10.51] class: 1
|   |   |   |--- lead_time >  92.50
|   |   |   |   |--- avg_price_per_room <= 113.53
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |--- weights: [16.09, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |--- lead_time <= 116.50
|   |   |   |   |   |   |   |   |--- weights: [69.97, 71.82] class: 1
|   |   |   |   |   |   |   |--- lead_time >  116.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [54.58, 22.77] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  89.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 14.01] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |--- weights: [30.09, 1.75] class: 0
|   |   |   |   |--- avg_price_per_room >  113.53
|   |   |   |   |   |--- weights: [13.29, 29.78] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 3.50
|   |   |   |   |--- avg_price_per_room <= 135.83
|   |   |   |   |   |--- weights: [298.08, 52.55] class: 0
|   |   |   |   |--- avg_price_per_room >  135.83
|   |   |   |   |   |--- lead_time <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 178.78
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [31.49, 17.52] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- weights: [31.49, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  178.78
|   |   |   |   |   |   |   |--- weights: [9.10, 17.52] class: 1
|   |   |   |   |   |--- lead_time >  2.50
|   |   |   |   |   |   |--- weights: [5.60, 21.02] class: 1
|   |   |   |--- lead_time >  3.50
|   |   |   |   |--- avg_price_per_room <= 105.28
|   |   |   |   |   |--- lead_time <= 24.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [52.48, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [42.68, 54.30] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [24.49, 89.34] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [23.79, 5.26] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [60.88, 68.32] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [77.67, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  24.50
|   |   |   |   |   |   |--- avg_price_per_room <= 56.55
|   |   |   |   |   |   |   |--- arrival_date <= 15.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.90, 3.50] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.90, 17.52] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  15.50
|   |   |   |   |   |   |   |   |--- weights: [22.39, 1.75] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  56.55
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 72.78
|   |   |   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.79, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.10, 5.26] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [36.39, 59.56] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  72.78
|   |   |   |   |   |   |   |   |   |--- weights: [216.92, 632.37] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |--- weights: [83.97, 432.67] class: 1
|   |   |   |   |--- avg_price_per_room >  105.28
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- weights: [32.89, 136.63] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 161.83
|   |   |   |   |   |   |   |   |   |--- weights: [20.99, 8.76] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  161.83
|   |   |   |   |   |   |   |   |   |--- weights: [2.10, 12.26] class: 1
|   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |--- weights: [317.68, 2226.42] class: 1
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- lead_time <= 22.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- weights: [9.80, 12.26] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [32.89, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  22.50
|   |   |   |   |   |   |   |--- weights: [30.09, 87.59] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- lead_time <= 6.50
|   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |--- weights: [739.61, 68.32] class: 0
|   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |--- weights: [3.50, 7.01] class: 1
|   |   |   |--- lead_time >  6.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- weights: [282.69, 1.75] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- weights: [4.90, 7.01] class: 1
|   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |--- weights: [54.58, 22.77] class: 0
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- avg_price_per_room <= 118.74
|   |   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 42.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [62.28, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [473.72, 176.92] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [86.07, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  42.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.49
|   |   |   |   |   |   |   |   |   |--- weights: [62.28, 3.50] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.49
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 79.26
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.87
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  79.26
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |   |--- weights: [5.60, 38.54] class: 1
|   |   |   |   |   |--- avg_price_per_room >  118.74
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.58
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [97.26, 24.52] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.58
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [125.25, 80.58] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.40, 8.76] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [57.38, 17.52] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [67.87, 71.82] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  35.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.80, 36.79] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |--- lead_time <= 17.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.17, 38.54] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  17.50
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 126.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [128.75, 162.91] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  126.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.40, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [68.57, 147.14] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- lead_time <= 102.50
|   |   |   |   |   |   |   |   |--- weights: [50.38, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  102.50
|   |   |   |   |   |   |   |   |--- weights: [0.70, 10.51] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [1345.58, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |--- weights: [34.29, 3.50] class: 0
|   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 6.50
|   |   |   |   |   |   |   |   |   |--- weights: [16.09, 1.75] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  6.50
|   |   |   |   |   |   |   |   |   |--- weights: [67.87, 63.06] class: 0
|   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [46.18, 12.26] class: 0
|   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |--- weights: [42.68, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |--- arrival_month <= 10.50
|   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.37
|   |   |   |   |   |   |   |   |   |--- weights: [17.49, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.37
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 82.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.10, 15.77] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  82.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [143.44, 36.79] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [9.10, 10.51] class: 1
|   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |--- weights: [23.09, 22.77] class: 0
|   |   |   |   |   |--- arrival_month >  10.50
|   |   |   |   |   |   |--- weights: [37.09, 45.54] class: 1
|   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |--- weights: [51.08, 0.00] class: 0
|--- lead_time >  150.50
|   |--- avg_price_per_room <= 99.89
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 272.00
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- avg_price_per_room <= 81.76
|   |   |   |   |   |   |--- lead_time <= 161.50
|   |   |   |   |   |   |   |--- weights: [21.69, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  161.50
|   |   |   |   |   |   |   |--- weights: [57.38, 31.53] class: 0
|   |   |   |   |   |--- avg_price_per_room >  81.76
|   |   |   |   |   |   |--- weights: [23.09, 31.53] class: 1
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- lead_time <= 151.50
|   |   |   |   |   |   |--- weights: [0.70, 3.50] class: 1
|   |   |   |   |   |--- lead_time >  151.50
|   |   |   |   |   |   |--- weights: [53.88, 5.26] class: 0
|   |   |   |--- lead_time >  272.00
|   |   |   |   |--- weights: [32.19, 45.54] class: 1
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [9.80, 660.39] class: 1
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |   |--- weights: [32.89, 10.51] class: 0
|   |   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.25
|   |   |   |   |   |   |   |--- weights: [7.00, 157.65] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.25
|   |   |   |   |   |   |   |--- weights: [5.60, 0.00] class: 0
|   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 81.81
|   |   |   |   |   |   |   |--- weights: [59.48, 14.01] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  81.81
|   |   |   |   |   |   |   |--- weights: [107.76, 99.85] class: 0
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- weights: [17.49, 36.79] class: 1
|   |--- avg_price_per_room >  99.89
|   |   |--- no_of_special_requests <= 2.25
|   |   |   |--- arrival_month <= 11.50
|   |   |   |   |--- weights: [4.20, 1660.62] class: 1
|   |   |   |--- arrival_month >  11.50
|   |   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |   |--- weights: [25.89, 0.00] class: 0
|   |   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |   |--- weights: [6.30, 29.78] class: 1
|   |   |--- no_of_special_requests >  2.25
|   |   |   |--- weights: [21.69, 0.00] class: 0


#identifying the most important features in the final model
importances = final_model.feature_importances_
indices = np.argsort(importances)


#Visualizing the most important features in the final model
plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="violet", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
no_of_adults	36275.00000	NaN	NaN	NaN	1.84496	0.51871	0.00000	2.00000	2.00000	2.00000	4.00000
no_of_children	36275.00000	NaN	NaN	NaN	0.10528	0.40265	0.00000	0.00000	0.00000	0.00000	10.00000
no_of_weekend_nights	36275.00000	NaN	NaN	NaN	0.81072	0.87064	0.00000	0.00000	1.00000	2.00000	7.00000
no_of_week_nights	36275.00000	NaN	NaN	NaN	2.20430	1.41090	0.00000	1.00000	2.00000	3.00000	17.00000
type_of_meal_plan	36275	4	Meal Plan 1	27835	NaN	NaN	NaN	NaN	NaN	NaN	NaN
required_car_parking_space	36275.00000	NaN	NaN	NaN	0.03099	0.17328	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved	36275	7	Room_Type 1	28130	NaN	NaN	NaN	NaN	NaN	NaN	NaN
lead_time	36275.00000	NaN	NaN	NaN	85.23256	85.93082	0.00000	17.00000	57.00000	126.00000	443.00000
arrival_year	36275.00000	NaN	NaN	NaN	2017.82043	0.38384	2017.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	36275.00000	NaN	NaN	NaN	7.42365	3.06989	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	36275.00000	NaN	NaN	NaN	15.59700	8.74045	1.00000	8.00000	16.00000	23.00000	31.00000
market_segment_type	36275	5	Online	23214	NaN	NaN	NaN	NaN	NaN	NaN	NaN
repeated_guest	36275.00000	NaN	NaN	NaN	0.02564	0.15805	0.00000	0.00000	0.00000	0.00000	1.00000
no_of_previous_cancellations	36275.00000	NaN	NaN	NaN	0.02335	0.36833	0.00000	0.00000	0.00000	0.00000	13.00000
no_of_previous_bookings_not_canceled	36275.00000	NaN	NaN	NaN	0.15341	1.75417	0.00000	0.00000	0.00000	0.00000	58.00000
avg_price_per_room	36275.00000	NaN	NaN	NaN	103.42354	35.08942	0.00000	80.30000	99.45000	120.00000	540.00000
no_of_special_requests	36275.00000	NaN	NaN	NaN	0.61966	0.78624	0.00000	0.00000	0.00000	1.00000	5.00000
booking_status	36275	2	Not_Canceled	24390	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	0	1	2	0	224.00000	2018	10	2	0	0	0	65.00000	0.00000	0	0	0	0	0	0	0	0	0	0	0	0	1	0
1	2	0	2	3	0	5.00000	2018	11	6	0	0	0	106.68000	1.00000	0	0	0	1	0	0	0	0	0	0	0	0	0	1
2	2	0	2	1	0	1.00000	2018	2	28	0	0	0	60.00000	0.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
3	2	0	0	2	0	211.00000	2018	5	20	0	0	0	100.00000	0.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
4	2	0	1	1	0	48.00000	2018	4	11	0	0	0	94.50000	0.00000	1	0	0	1	0	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36270	2	0	2	6	0	85.00000	2018	8	3	0	0	0	167.80000	1.00000	0	0	0	0	0	0	1	0	0	0	0	0	0	1
36271	2	0	1	3	0	228.00000	2018	10	17	0	0	0	90.95000	2.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
36272	2	0	2	6	0	148.00000	2018	7	1	0	0	0	98.39000	2.00000	0	0	0	0	0	0	0	0	0	0	0	0	0	1
36273	2	0	0	3	0	63.00000	2018	4	21	0	0	0	94.50000	0.00000	1	0	0	1	0	0	0	0	0	0	0	0	0	1
36274	2	0	1	2	0	207.00000	2018	12	30	0	0	0	161.67000	0.00000	0	0	0	0	0	0	0	0	0	0	0	0	1	0

	no_of_adults	no_of_children	no_of_weekend_nights	no_of_week_nights	required_car_parking_space	lead_time	arrival_year	arrival_month	arrival_date	repeated_guest	no_of_previous_cancellations	no_of_previous_bookings_not_canceled	avg_price_per_room	no_of_special_requests	booking_status	type_of_meal_plan_Meal Plan 2	type_of_meal_plan_Meal Plan 3	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 3	room_type_reserved_Room_Type 4	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	room_type_reserved_Room_Type 7	market_segment_type_Complementary	market_segment_type_Corporate	market_segment_type_Offline	market_segment_type_Online
0	2	0	1	2	0	224.00000	2018	10	2	0	0	0	65.00000	0.00000	0	0	0	0	0	0	0	0	0	0	0	0	1	0
1	2	0	2	3	0	5.00000	2018	11	6	0	0	0	106.68000	1.00000	0	0	0	1	0	0	0	0	0	0	0	0	0	1
2	2	0	2	1	0	1.00000	2018	2	28	0	0	0	60.00000	0.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
3	2	0	0	2	0	211.00000	2018	5	20	0	0	0	100.00000	0.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
4	2	0	1	1	0	48.00000	2018	4	11	0	0	0	94.50000	0.00000	1	0	0	1	0	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36270	2	0	2	6	0	85.00000	2018	8	3	0	0	0	167.80000	1.00000	0	0	0	0	0	0	1	0	0	0	0	0	0	1
36271	2	0	1	3	0	228.00000	2018	10	17	0	0	0	90.95000	2.00000	1	0	0	0	0	0	0	0	0	0	0	0	0	1
36272	2	0	2	6	0	148.00000	2018	7	1	0	0	0	98.39000	2.00000	0	0	0	0	0	0	0	0	0	0	0	0	0	1
36273	2	0	0	3	0	63.00000	2018	4	21	0	0	0	94.50000	0.00000	1	0	0	1	0	0	0	0	0	0	0	0	0	1
36274	2	0	1	2	0	207.00000	2018	12	30	0	0	0	161.67000	0.00000	0	0	0	0	0	0	0	0	0	0	0	0	1	0

	count	mean	std	min	25%	50%	75%	max
no_of_adults	25660.00000	2.00000	0.00000	2.00000	2.00000	2.00000	2.00000	2.00000
no_of_children	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_weekend_nights	25660.00000	0.88581	0.88374	0.00000	0.00000	1.00000	2.00000	5.00000
no_of_week_nights	25660.00000	2.23313	1.36900	0.00000	1.00000	2.00000	3.00000	6.00000
required_car_parking_space	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
lead_time	25660.00000	66.21904	67.25142	0.00000	12.00000	44.00000	100.00000	289.50000
arrival_year	25660.00000	2018.00000	0.00000	2018.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	25660.00000	7.32720	3.13823	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	25660.00000	15.73566	8.85085	1.00000	8.00000	16.00000	23.00000	31.00000
repeated_guest	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_previous_cancellations	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_previous_bookings_not_canceled	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
avg_price_per_room	25660.00000	105.24561	33.81945	20.75000	80.75000	100.00000	127.00000	179.55000
no_of_special_requests	25660.00000	0.72697	0.77004	0.00000	0.00000	1.00000	1.00000	2.50000
booking_status	25660.00000	0.28757	0.45264	0.00000	0.00000	0.00000	1.00000	1.00000
type_of_meal_plan_Meal Plan 2	25660.00000	0.04373	0.20449	0.00000	0.00000	0.00000	0.00000	1.00000
type_of_meal_plan_Meal Plan 3	25660.00000	0.00019	0.01396	0.00000	0.00000	0.00000	0.00000	1.00000
type_of_meal_plan_Not Selected	25660.00000	0.17217	0.37754	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 2	25660.00000	0.02202	0.14675	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 3	25660.00000	0.00023	0.01529	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 4	25660.00000	0.20951	0.40697	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 5	25660.00000	0.00892	0.09405	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 6	25660.00000	0.03648	0.18748	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 7	25660.00000	0.00604	0.07749	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Complementary	25660.00000	0.01325	0.11435	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Corporate	25660.00000	0.05171	0.22145	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Offline	25660.00000	0.15877	0.36547	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Online	25660.00000	0.77241	0.41929	0.00000	1.00000	1.00000	1.00000	1.00000

	count	mean	std	min	25%	50%	75%	max
no_of_adults	25660.00000	2.00000	0.00000	2.00000	2.00000	2.00000	2.00000	2.00000
no_of_children	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_weekend_nights	25660.00000	0.88581	0.88374	0.00000	0.00000	1.00000	2.00000	5.00000
no_of_week_nights	25660.00000	2.23313	1.36900	0.00000	1.00000	2.00000	3.00000	6.00000
required_car_parking_space	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
lead_time	25660.00000	66.21904	67.25142	0.00000	12.00000	44.00000	100.00000	289.50000
arrival_year	25660.00000	2018.00000	0.00000	2018.00000	2018.00000	2018.00000	2018.00000	2018.00000
arrival_month	25660.00000	7.32720	3.13823	1.00000	5.00000	8.00000	10.00000	12.00000
arrival_date	25660.00000	15.73566	8.85085	1.00000	8.00000	16.00000	23.00000	31.00000
repeated_guest	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_previous_cancellations	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
no_of_previous_bookings_not_canceled	25660.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000	0.00000
avg_price_per_room	25660.00000	105.24561	33.81945	20.75000	80.75000	100.00000	127.00000	179.55000
no_of_special_requests	25660.00000	0.72697	0.77004	0.00000	0.00000	1.00000	1.00000	2.50000
booking_status	25660.00000	0.28757	0.45264	0.00000	0.00000	0.00000	1.00000	1.00000
type_of_meal_plan_Meal Plan 2	25660.00000	0.04373	0.20449	0.00000	0.00000	0.00000	0.00000	1.00000
type_of_meal_plan_Meal Plan 3	25660.00000	0.00019	0.01396	0.00000	0.00000	0.00000	0.00000	1.00000
type_of_meal_plan_Not Selected	25660.00000	0.17217	0.37754	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 2	25660.00000	0.02202	0.14675	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 3	25660.00000	0.00023	0.01529	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 4	25660.00000	0.20951	0.40697	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 5	25660.00000	0.00892	0.09405	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 6	25660.00000	0.03648	0.18748	0.00000	0.00000	0.00000	0.00000	1.00000
room_type_reserved_Room_Type 7	25660.00000	0.00604	0.07749	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Complementary	25660.00000	0.01325	0.11435	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Corporate	25660.00000	0.05171	0.22145	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Offline	25660.00000	0.15877	0.36547	0.00000	0.00000	0.00000	0.00000	1.00000
market_segment_type_Online	25660.00000	0.77241	0.41929	0.00000	1.00000	1.00000	1.00000	1.00000

	Booking_ID	no_of_adults	no_of_weekend_nights	no_of_week_nights	type_of_meal_plan	room_type_reserved	lead_time	arrival_year	arrival_month	arrival_date	market_segment_type	avg_price_per_room	no_of_special_requests	booking_status
0	INN00001	2	1	2	Meal Plan 1	Room_Type 1	224	2017	10	2	Offline	65.00000	0	Not_Canceled
1	INN00002	2	2	3	Not Selected	Room_Type 1	5	2018	11	6	Online	106.68000	1	Not_Canceled
2	INN00003	1	2	1	Meal Plan 1	Room_Type 1	1	2018	2	28	Online	60.00000	0	Canceled
3	INN00004	2	0	2	Meal Plan 1	Room_Type 1	211	2018	5	20	Online	100.00000	0	Canceled
4	INN00005	2	1	1	Not Selected	Room_Type 1	48	2018	4	11	Online	94.50000	0	Canceled

	col	Adj. R-squared after_dropping col	RMSE after dropping col
0	no_of_children	0.31587	0.37356
1	required_car_parking_space	0.31587	0.37356

	col	Adj. R-squared after_dropping col	RMSE after dropping col
0	market_segment_type_Online	0.31571	0.37360
1	market_segment_type_Offline	0.31534	0.37370

	no_of_weekend_nights	no_of_week_nights	lead_time	arrival_year	arrival_month	avg_price_per_room	no_of_special_requests	type_of_meal_plan_Not Selected	room_type_reserved_Room_Type 2	room_type_reserved_Room_Type 5	room_type_reserved_Room_Type 6	market_segment_type_Corporate	market_segment_type_Online
Odds	1.10149	1.08037	1.01651	0.99752	0.95145	1.01866	0.22157	1.47979	0.74036	0.62762	0.74699	3.55721	8.86423
Change_odd%	10.14875	8.03692	1.65079	-0.24843	-4.85468	1.86632	-77.84298	47.97938	-25.96418	-37.23799	-25.30128	255.72052	786.42339

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.64 Threshold	Logistic Regression-0.29 Threshold
Accuracy	0.81155	0.78748	0.78221
Recall	0.56290	0.38677	0.76204
Precision	0.71613	0.77353	0.59206
F1	0.63034	0.51569	0.66638

	Logistic Regression-default Threshold (0.5)	Logistic Regression-0.64 Threshold	Logistic Regression-0.29 Threshold
Accuracy	0.80060	0.78748	0.76929
Recall	0.56261	0.38677	0.75533
Precision	0.69730	0.77353	0.58134
F1	0.62276	0.51569	0.65701

	ccp_alphas	impurities
0	0.00000	0.00345
1	-0.00000	0.00345
2	-0.00000	0.00345
3	-0.00000	0.00345
4	-0.00000	0.00345
...	...	...
1475	0.00563	0.33245
1476	0.00760	0.34005
1477	0.01487	0.35492
1478	0.04504	0.44500
1479	0.05500	0.50000

	Decision Tree without class_weight	Decision Tree with class_weight	Decision Tree (Pre-Pruning)	decision_tree_post_train
Accuracy	0.99655	0.99655	0.64681	0.81912
Recall	0.98791	1.00000	0.79735	0.85879
Precision	1.00000	0.98805	0.43522	0.63554
F1	0.99392	0.99399	0.56309	0.73049

	Decision Tree without class_weight	Decision Tree with class_weight	Decision Tree (Pre-Pruning)	decision_tree_post_test
Accuracy	0.79150	0.79488	0.64315	0.79956
Recall	0.64298	0.63988	0.80373	0.81572
Precision	0.64384	0.65233	0.43985	0.61956
F1	0.64341	0.64604	0.56856	0.70424

INN Hotels Project¶

Context¶

Objective¶

Data Description¶

Importing necessary libraries and data¶

Loading the dataset¶

Data Overview¶

View the first and last 5 rows of the dataset.¶

Understand the shape of the data¶

Check the data types of the columns for the dataset.¶

Checking for missing values¶

Dropping duplicate values¶

Statistical summary of data.¶

Exploratory Data Analysis (EDA)¶

CUSTOM FUNCTION TO CARRY OUT EDA¶

UNIVARIATE ANALYSIS¶

Visualizing features with countinous variables¶

Visualizing features with Categorical variables¶

BIVARIATE ANALYSIS¶

Correlation heat map¶

Average room price vs market segment¶

no_of_previous_cancellations vs repeated_guest¶

no_of_special_requests vs repeated_guest¶

Booking status vs lead time¶

Booking status vs market segment¶

Booking status vs Arrival months¶

Data Preprocessing¶

1. Missing value treatment.¶

2. Feature Enginearing¶

3. Outlier Detection.¶

Managing ouliers¶

4. Data preparation for model.¶

EDA¶

defining custom function to carryout EDA¶

Univariate analysis¶

Visualizing continous variables¶

Visualizing Categorical variables.¶

Bivariate analysis¶

Correlation heat map¶

Plotting arrival month per room vs market segment type.¶

Plotting number of special requests vs booking status.¶

Plotting segment_type_Online vs booking status.¶

Average price per room vs Booking status¶

Lead time vs booking status¶

Managing multicolinearity.¶

Checking multicolinearity¶

Treating Multicolinearities¶

Building a Logistic Regression model¶

Model performance evaluation(Logistic Regression)¶

Removing high P-values¶

Coeffiecient interpretations.¶

Checking model performance.¶

Optimizing model performance(Logistics Regression)¶

PRECISION-RECALL CURVE¶

Optimal threshold using AUC¶

Final Logistic Regression Model Summary¶

Final model coefficient interpretation¶

Building a Decision Tree model(Default)¶

Model Evaluation.¶

Do we need to prune the tree?¶

Decision Tree(with class_weights)¶

Decision tree(Pre-prunning)¶

Decision tree(post prunning)¶

Model Performance Comparison(Decision tree)¶

Model performance comparisms¶

Actionable Insights and Recommendations¶

Final model performance selection.¶

Final model prediction and insights¶

Intepretation of Prediction and insights¶

Recommendations¶